Vectorized SVE2 Optimization of the Post-Quantum Signature ML-DSA on ARMv9-A Architecture

Post-quantum cryptography (PQC) is essential to securing data in the quantum computing era, and standardization efforts led by NIST have driven extensive research on practical and efficient implementations. With the emerging deployment of ARMv9-A processors in mobile and edge devices, optimizing PQC algorithms for this architecture is becoming increasingly important. Among the NIST-selected digital signature schemes, ML-DSA stands out due to its strong security and efficiency, making it suitable for general-purpose deployment. In this work, we present a highly optimized implementation of ML-DSA for the ARMv9-A architecture, leveraging the SVE2 vector instruction set. We propose a vector-friendly sparse polynomial multiplication scheme and adapt an early-check mechanism that significantly reduces redundant computation in the signature validity check. We also design a tailored conditional instruction pipeline to further enhance efficiency. Our implementation achieves a 70.7% cycle reduction in signature generation compared to the baseline implementation, establishing the first highly vectorized ML-DSA implementation on ARMv9-A using the SVE2 extension. These results demonstrate the practicality of deploying high-performance post-quantum signatures on next-generation mobile and edge platforms.